Yun Chen
Yun Chen is a graduate student in Pattern Recognition and Intelligent System Laboratory (PRIS Lab) at Beijing University of Posts and Telecommunications, and an AI Resident at Uber ATG Toronto, supervised by Prof. Raquel Urtasun. His research interests include Computer Vision especially Deep Learning.
Research Papers
Learning Joint 2D-3D Representations for Depth Completion
Y. Chen, B. Yang, M. Liang, R. Urtasun
We design a simple yet effective architecture that fuses information between 2D and 3D representations at multiple levels to learn fully fused joint representations at multiple levels, and show state-of-the-art results on the KITTI depth completion benchmark. [PDF]
International Conference on Computer Vision (ICCV), 2019
We design a simple yet effective architecture that fuses information between 2D and 3D representations at multiple levels to learn fully fused joint representations at multiple levels, and show state-of-the-art results on the KITTI depth completion benchmark. [PDF]
International Conference on Computer Vision (ICCV), 2019
Multi-Task Multi-Sensor Fusion for 3D Object Detection
M. Liang, B. Yang, Y. Chen, R. Hu, R. Urtasun
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019